Estimating mortality associated with seasonal influenza among adults aged 65 years and above in China from 2011 to 2016: A systematic review and model analysis

Abstract Background Estimation of influenza disease burden is crucial for optimizing intervention strategies against seasonal influenza. This study aimed to estimate influenza‐associated excess respiratory and circulatory (R&C) and all‐cause (AC) mortality among older adults aged 65 years and above in mainland China from 2011 to 2016. Methods Through a systematic review, we collected influenza‐associated excess R&C and AC mortality data of older adults aged 65 years and above for specific cities/provinces in mainland China. Generalized linear models were fitted to estimate the corresponding excess mortality for older adults by province and nationwide, accounting for the potential variables of influenza virus activity, demography, economics, meteorology, and health service. All statistical analyses were conducted using R software. Results A total of 9154 studies were identified in English and Chinese databases, and 11 (0.1%) were included in the quantitative synthesis after excluding duplicates and screening the title, abstract, and full text. Using a generalized linear model, the estimates of annual national average influenza‐associated excess R&C and AC mortality among older adults aged 65 years and above were 111.8 (95% CI: 92.8–141.1) and 151.6 (95% CI: 127.6–179.3) per 100,000 persons, respectively. Large variations in influenza‐associated excess R&C and AC mortality among older adults were observed among 30 provinces. Conclusions Influenza was associated with substantial excess R&C and AC mortality among older adults aged 65 years and above in China from 2011 to 2016. This analysis provides valuable evidence for the introduction of the influenza vaccine into the National Immunization Program for the elderly in China.


| INTRODUCTION
The annual circulation of seasonal influenza causes a heavy disease burden on society, with 4 to 23 million lower respiratory tract infection hospitalizations and 290,000-650,000 respiratory deaths globally, of which more than half of deaths occur in the population aged 65 years and above. 1,2 In China, the burden of influenza-associated deaths is also serious, with an average of 88,000 (95% confidence interval [CI]: 84,000-92,000) excess influenza-associated respiratory deaths annually, 3 80% to 95% occurring in older adults. [3][4][5][6] Vaccination is a core pharmaceutical preventative intervention to protect against influenza virus infection or severe outcomes after infection. However, the influenza vaccine is not included in the National Immunization Program in China, and vaccination is paid out of pocket in the majority of regions, 7 causing an extremely low uptake (3.8%) among older adults aged 60 years and above nationwide. 8 As the most populous country with 191 million older adults 65 years of age or above, 9 a reliable estimate of excess mortality associated with influenza in this population (particularly after the 2009 influenza pandemic, as the burden may change due to the displacement of seasonal A(H1N1) virus after the pandemic 3,10 ) is critical for policy-making about immunization programs.
It is difficult to quantify the exact mortality burden of influenza for the following reasons: routine laboratory tests are rarely conducted across China, and secondary bacterial infections or exacerbation of existing underlying conditions triggered by influenza virus infections, can lead to death. 11 Accordingly, influenza mortality burden studies mainly rely on applying statistical methods to estimate broadly defined disease outcomes, such as mortality from respiratory diseases, respiratory and circulatory (R&C) diseases, or all-cause (AC) mortality. 12,13 None of these metrics could independently depict the whole picture of the mortality burden, with excess mortality attributable to respiratory diseases having the highest specificity, excess AC mortality having the highest sensitivity, and excess R&C mortality in between. 14 To our knowledge, Li et al estimated the national and provincial influenza-associated excess respiratory mortality in older adults after the 2009 influenza pandemic in mainland China. 3 For influenzaassociated excess R&C or AC mortality, a few studies estimated mortality at the national or regional level before the 2009 influenza pandemic 4,10 or at the city level, such as Beijing, 15 Shanghai, 16,17 and Shenzhen. 18 To better understand the post-pandemic influenzaassociated mortality burden among elderly individuals aged 65 years and above in mainland China, we used a systematic review and model method to estimate influenza-associated excess R&C and AC mortality by province and nationwide from 2011 to 2016.

| METHODS
Through a systematic review, we collected influenza-associated excess R&C and AC mortality data of older adults aged 65 years and above in specific cities/provinces in mainland China. Then, we used a correlation analysis to explore the potential factors that influence influenza-associated mortality, including influenza virus activity, demographics, economics, meteorology, and health services. Based on the results of the correlation analysis, we used generalized linear models to estimate the corresponding excess mortality stratified by province, accounting for the potential variables.

| Systematic review
We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines 19  was developed and adapted for each database with a combination of keywords in MeSH, title/abstract, and all fields. Keywords included "influenza," "burden," "mortality," and "China," with many other closely related words and synonyms along with their Chinese translations (Table S1). The reference lists of all eligible articles were also searched.
Each article identified through the search strategy underwent a process of title, abstract, and full-text screening based upon a set of inclusion and exclusion criteria (Table S2). Eligible articles were those reporting influenza-associated excess mortality among older adults aged 65 years and above in specific cities/provinces in mainland China. Studies were excluded if (1) they did not report the target population or deaths from R&C or AC diseases; (2) they did not include seasonal influenza; (3) population-based estimates of influenzaassociated excess mortality burden could not be derived; and (4) they were systematic reviews, meta-analyses, conference proceedings, commentaries, editorials, or letters. Studies with a time frame combining both seasonal and pandemic influenza periods were included, but we excluded the data for the 2009 H1N1 pandemic.
We extracted publication information, methodological characteristics and outcome measurements from the included studies (Supporting Information p. 4; Table S3). To evaluate the quality of the included studies, we also developed a scoring system with five criteria, including publication, data reliability, regional representativeness, modeling method and result precision, with each score ranging from 0 to 1 point. Studies with scores of 3.5 or more, which were considered high-quality studies, were included in the modeling process to ensure the accuracy of the estimates (Table S4). Literature screening, scoring, and data extraction were all carried out and cross-checked by two researchers (K.D. and G.Z.), and the research team corrected any cases of inconformity (J.Y. and H.G.).

| Influenza virological data
To describe the influenza circulation intensity and pattern, annual influenza virological data (i.e., positive rate of influenza among all age groups by type and that among older adults aged 65 years and above) in 30 provinces (excluding Tibet due to limited sentinel hospitals) from 2005 to 2016 were extracted from the Influenza Weekly Report by the Chinese National Influenza Center and published literatures. 3,[20][21][22][23][24] In addition, we systematically searched two Chinese peer-reviewed databases (CNKI and Wanfang) to collect annual influenza virus-positive rates by type in specific provinces before 2005 (Table S5). [25][26][27][28] However, the National Influenza Surveillance Network was not expanded until 2009, and positive rates of older adults aged 65 years and above were inaccurate due to the small sample size before 2009; thus, we used positive rates of all age groups to replace the positive rates of older adults aged 65 years and above before 2009 in the model fit.

| Other parameters
We obtained province-stratified demographic data (number and proportion of rural residents) and economic data (gross domestic product  30 We used meteorological data from all monitoring stations within the same province to estimate temperature and relative humidity for the province as a whole and estimated absolute humidity based on ambient temperature and relative humidity. 31 We extracted medical service data from the China Health Statisti-

| Statistical analysis
To explore the potential impact of the aforementioned predictor variables on influenza-associated excess R&C mortality, we conducted a correlation analysis between these predictor variables and excess R&C mortality. Then, based on the results of the correlation analysis, considering that influenza-associated excess R&C mortality approximately follows a normal distribution, 31,32 we applied a generalized linear model method to estimate influenza-associated excess R&C mortality, with a natural log link to avoid generating negative estimates of excess mortality, 33  were estimated using the annual average variables over 6 years at the provincial and national levels, respectively. The 95% CI for influenzaassociated excess R&C mortality was obtained using the bootstrap method, whereby the model was simulated 2000 times. Likewise, excess AC mortality was estimated.
To explore the impact of data quality, different models and extremely low excess mortality on the results, we conducted the following sensitivity analyses: (1) using higher quality data (i.e., setting higher scoring criteria of no lower than 4) to fit the model to estimate influenza-associated excess R&C and AC mortality; (2) applying a random forest approach to estimate the results; and (3) (Table S7).   Figure 2B).

| Sensitivity analyses
For the influenza-associated excess R&C mortality among older adults aged 65 years and above, the sensitivity analyses using higher quality data, applying the random forest approach, and removing the

| Internal and external comparisons of influenza-associated excess mortality among older adults aged 65 years and above between the estimates and published results
The cross validation showed that the overall RMSE of the main analysis was slightly less than that of the sensitivity analyses, and the MAE was similar between the main analysis and the sensitivity analyses (Tables S9 and S10). The internal comparison demonstrated that the majority of original annual influenza excess R&C and AC mortality in Beijing, Guangdong, Yunnan, and Zhejiang Provinces, which were obtained in previous publications, 18,[37][38][39][40] were similar to our corresponding estimates (Figures S3 and S4). The external comparison showed consistency in average influenza excess R&C and AC mortality in multiple seasons between our estimates and those obtained in previous publications in Chongqing and Shanghai (Figures S5 and   S6). 16,17,36 Furthermore, we also found that the large spatial variations in influenza-associated excess R&C and AC mortality among the provinces followed the same trend as the variations observed in influenzaassociated excess respiratory mortality among provinces in China from a previous study ( Figure S7) Our excess mortality estimates attributable to R&C and AC diseases among older adults is consistent with the influenza-associated excess mortality burden estimated before the 2009 pandemic in China in a previous study, despite using a different modeling approach. 10 Our estimates have some differences from those in other countries/areas (Table S11). Our estimate for R&C was higher than that reported in Colorado, a state in western America 41 ; Shanghai, a subtropical city in eastern China 17 ; and Yancheng, a subtropical city in eastern China, 42 but lower than that reported in India 43 and Shenzhen, a tropical city in China. 18 Our estimate for AC diseases was higher than that reported in Europe, 44 Greece, 45 Denmark, and Hong Kong. 46,47 Possible explanations for the differences might be the regional variations in socioeconomic and demographic factors, 10,48,49 medical resource utilization, diagnostic criteria for the recorded cause of death, 50-52 different models used, 11,53 and different study periods. 6 mortality burdens in rural areas are significantly higher than those in urban areas. 10 In Guangdong, the high mortality burden might be attributable to its high outpatient burden and absolute humidity.
There was a significant positive correlation between the intensity of influenza virus activity and outpatient visits. The high outpatient burden might indicate high influenza virus activity in Guangdong Province, which causes a high mortality burden. 56,57 In addition, the high absolute humidity might exacerbate the mortality burden. [58][59][60] Likewise, the lowest excess R&C and AC mortality burden in Tianjin, Beijing, and Shanghai municipalities would be related to their low proportion of rural residents. In addition, higher socioeconomic development, better medical resources, and higher influenza vaccine replacement has had an impact on influenza-associated mortality has not been fully addressed. The influenza-associated excess AC and the influenza-associated pneumonia and influenza mortality of the post pandemic were lower than that of the pre pandemic in England and Wales. 67 However, the influenza-associated respiratory, cardiovascular, and AC mortality rates during the post pandemic were similar to those of the pre pandemic in Mexico. 49 And a systematic review demonstrated no difference in the influenza-associated excess mortality between the pre pandemic and post pandemic in China. 68 The circulation intensity of influenza virus by type (influenza A and influenza B) was used as a covariate in the model. However, the circulation intensity stratified by influenza A subtype (i.e., seasonal A/H1N1, A/H1N1pdm09, and H3N2) was not used in the model due to data unavailability. Accordingly, the potential impact of A/H1N1pdm09 replacement was only partially accounted for in this study. Additional studies on this topic are of interest. Fifth, the potential time trend might mainly influence the mortality of the population and the intensity of influenza virus activity in our study. The overall mortality rate of the whole population was relatively stable, with little change, ranging from 7.04 to 7.14 per 1000 persons. 9 In addition, influenzaassociated mortality is strongly correlated with the intensity of influenza virus activity, which can reflect changes in time trends of influenza-associated mortality burden. Although the intensity of influenza virus activity is seasonal in China, 69 we used an annual indicator of influenza activity intensity in our model without considering its variation within the year. Therefore, we did not consider any other time trend effect in the model. Finally, pathogens such as respiratory syncytial virus, which was suggested in previous studies to be associated with increased mortality in adults, 54,70 were not included in our model.

| CONCLUSION
This study demonstrated a substantial influenza-associated mortality burden among older adults aged 65 years and above in 30 provinces in China from 2011 to 2016. Differences in demographic, meteorological, socioeconomic, and medical resource factors could explain spatial variations in influenza-associated mortality burden for older adults aged 65 years and above among provinces. Our estimates of the influenza-associated excess R&C and AC mortality provided valuable information to support policy-makers in including influenza vaccination for older adults in the National Immunization Program to promote medical equity in China. writing-review and editing.

DATA AVAILABILITY STATEMENT
The data that support the findings of this study are openly available on GitHub at https://github.com/lvyihongning/Influenza-associatedmortality-data.